Senior MLOps, Data Systems Engineer

🕒 May 11

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Lime

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2017

🚗 Transport

🛍️ eCommerce

☁️ SaaS

💰 $418M Convertible Note on 2021-11

Transport • eCommerce • SaaS

Lime is a transportation company that provides shared electric scooters and bikes in urban areas, making it easy for users to travel short distances. With a focus on safety, sustainability, and community, Lime aims to offer an eco-friendly alternative to traditional transportation methods. Users can easily locate and rent vehicles through the Lime app, promoting responsible riding and parking practices to enhance the urban mobility experience.

📋 Description

• ML Pipeline & Data Systems Development: Design, build, and maintain scalable pipelines that span data ingestion, annotation, validation, training, evaluation, and deployment, ensuring reproducibility, consistency, and traceability across the full ML lifecycle. • Data & Annotation Pipeline Integration: Build and integrate annotation workflows with upstream data ingestion and training systems, enabling efficient task creation, labeling, QA, and dataset updates that directly support model iteration. • Data-Centric Iteration: Analyze model performance and failures, and drive targeted data improvements by connecting production signals, data mining, and annotation workflows into continuous feedback loops. • Experimentation & Reproducibility: Implement systems for experiment tracking, dataset versioning, and model lineage to enable reliable comparison and iteration across experiments. • CI/CD for Machine Learning: Develop and maintain CI/CD workflows tailored to ML systems, enabling automated testing, validation, and deployment of models and pipelines. • Model Deployment Support: Collaborate with embedded and platform teams to support the deployment of models to edge environments, ensuring compatibility, performance, and reliability. • Monitoring & Feedback Loops: Implement monitoring, logging, and feedback systems to track model performance in production and drive continuous improvement through data and model iteration. • Compute Optimization: Optimize training and inference workflows across cloud environments, including efficient utilization of GPU and compute resources. • Cross-Functional Collaboration: Work closely with applied scientists, embedded engineers, and data teams to ensure alignment across data workflows, model development, and deployment systems. • End-to-End Contribution: Participate in and improve the full ML lifecycle, from raw data ingestion and annotation through training, evaluation, deployment support, and post-deployment analysis.

🎯 Requirements

• 5+ years of industry experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or related roles. • Strong programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow. • Experience building and maintaining end-to-end ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows. • Experience designing or integrating annotation and data curation workflows, and understanding how labeled data impacts model performance. • Strong understanding of dataset versioning, data lineage, and reproducibility in machine learning systems. • Experience with experiment tracking and model lifecycle management. • Familiarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and applying them to machine learning workflows. • Experience with containerization (Docker) and workflow orchestration systems. • Experience with cloud-based ML environments (e.g., AWS) and distributed training workflows. • Strong understanding of real-world data challenges, including noisy inputs, edge cases, and variability across environments. • Strong problem-solving and debugging skills, particularly in complex, multi-stage systems. • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).

🏖️ Benefits

• Offers Equity • Offers Bonus